Overview

Dataset statistics

Number of variables26
Number of observations400
Missing cells612
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory64.2 KiB
Average record size in memory164.3 B

Variable types

Numeric12
Categorical14

Alerts

wc has a high cardinality: 92 distinct valuesHigh cardinality
id is highly overall correlated with al and 3 other fieldsHigh correlation
al is highly overall correlated with id and 2 other fieldsHigh correlation
su is highly overall correlated with bgrHigh correlation
bgr is highly overall correlated with suHigh correlation
bu is highly overall correlated with sc and 1 other fieldsHigh correlation
sc is highly overall correlated with id and 2 other fieldsHigh correlation
sod is highly overall correlated with hemoHigh correlation
pot is highly overall correlated with pcv and 1 other fieldsHigh correlation
hemo is highly overall correlated with id and 6 other fieldsHigh correlation
dm is highly overall correlated with htn and 1 other fieldsHigh correlation
pc is highly overall correlated with al and 3 other fieldsHigh correlation
pcc is highly overall correlated with pcHigh correlation
pcv is highly overall correlated with pot and 3 other fieldsHigh correlation
rc is highly overall correlated with pot and 2 other fieldsHigh correlation
htn is highly overall correlated with dm and 1 other fieldsHigh correlation
cad is highly overall correlated with dm and 1 other fieldsHigh correlation
appet is highly overall correlated with pe and 1 other fieldsHigh correlation
pe is highly overall correlated with appet and 1 other fieldsHigh correlation
ane is highly overall correlated with appet and 1 other fieldsHigh correlation
classification is highly overall correlated with id and 3 other fieldsHigh correlation
pcc is highly imbalanced (51.5%)Imbalance
ba is highly imbalanced (69.3%)Imbalance
cad is highly imbalanced (74.5%)Imbalance
appet is highly imbalanced (52.3%)Imbalance
pe is highly imbalanced (54.2%)Imbalance
ane is highly imbalanced (60.0%)Imbalance
bgr has 44 (11.0%) missing valuesMissing
bu has 19 (4.8%) missing valuesMissing
sc has 17 (4.2%) missing valuesMissing
sod has 87 (21.8%) missing valuesMissing
pot has 88 (22.0%) missing valuesMissing
hemo has 52 (13.0%) missing valuesMissing
pcv has 70 (17.5%) missing valuesMissing
wc has 105 (26.2%) missing valuesMissing
rc has 130 (32.5%) missing valuesMissing
id is uniformly distributedUniform
id has unique valuesUnique
al has 245 (61.3%) zerosZeros
su has 339 (84.8%) zerosZeros

Reproduction

Analysis started2023-09-13 17:59:11.177266
Analysis finished2023-09-13 17:59:26.820793
Duration15.64 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.5
Minimum0
Maximum399
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-09-13T23:29:26.902609image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.95
Q199.75
median199.5
Q3299.25
95-th percentile379.05
Maximum399
Range399
Interquartile range (IQR)199.5

Descriptive statistics

Standard deviation115.6143
Coefficient of variation (CV)0.57952031
Kurtosis-1.2
Mean199.5
Median Absolute Deviation (MAD)100
Skewness0
Sum79800
Variance13366.667
MonotonicityStrictly increasing
2023-09-13T23:29:27.010955image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.2%
263 1
 
0.2%
273 1
 
0.2%
272 1
 
0.2%
271 1
 
0.2%
270 1
 
0.2%
269 1
 
0.2%
268 1
 
0.2%
267 1
 
0.2%
266 1
 
0.2%
Other values (390) 390
97.5%
ValueCountFrequency (%)
0 1
0.2%
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
ValueCountFrequency (%)
399 1
0.2%
398 1
0.2%
397 1
0.2%
396 1
0.2%
395 1
0.2%
394 1
0.2%
393 1
0.2%
392 1
0.2%
391 1
0.2%
390 1
0.2%

age
Real number (ℝ)

Distinct76
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.675
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-09-13T23:29:27.119059image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile19
Q142
median55
Q364
95-th percentile74.05
Maximum90
Range88
Interquartile range (IQR)22

Descriptive statistics

Standard deviation17.022008
Coefficient of variation (CV)0.32940508
Kurtosis0.12624968
Mean51.675
Median Absolute Deviation (MAD)10
Skewness-0.70136431
Sum20670
Variance289.74875
MonotonicityNot monotonic
2023-09-13T23:29:27.216342image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 28
 
7.0%
65 17
 
4.2%
48 12
 
3.0%
55 12
 
3.0%
50 12
 
3.0%
47 11
 
2.8%
62 10
 
2.5%
56 10
 
2.5%
59 10
 
2.5%
54 10
 
2.5%
Other values (66) 268
67.0%
ValueCountFrequency (%)
2 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 2
0.5%
6 1
 
0.2%
7 1
 
0.2%
8 3
0.8%
11 1
 
0.2%
12 2
0.5%
14 1
 
0.2%
ValueCountFrequency (%)
90 1
 
0.2%
83 1
 
0.2%
82 1
 
0.2%
81 1
 
0.2%
80 4
1.0%
79 1
 
0.2%
78 1
 
0.2%
76 5
1.2%
75 5
1.2%
74 3
0.8%

bp
Real number (ℝ)

Distinct10
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.575
Minimum50
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-09-13T23:29:27.299425image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q170
median80
Q380
95-th percentile100
Maximum180
Range130
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.489785
Coefficient of variation (CV)0.17616435
Kurtosis8.9039621
Mean76.575
Median Absolute Deviation (MAD)10
Skewness1.6018578
Sum30630
Variance181.97431
MonotonicityNot monotonic
2023-09-13T23:29:27.373611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
80 128
32.0%
70 112
28.0%
60 71
17.8%
90 53
13.2%
100 25
 
6.2%
50 5
 
1.2%
110 3
 
0.8%
140 1
 
0.2%
180 1
 
0.2%
120 1
 
0.2%
ValueCountFrequency (%)
50 5
 
1.2%
60 71
17.8%
70 112
28.0%
80 128
32.0%
90 53
13.2%
100 25
 
6.2%
110 3
 
0.8%
120 1
 
0.2%
140 1
 
0.2%
180 1
 
0.2%
ValueCountFrequency (%)
180 1
 
0.2%
140 1
 
0.2%
120 1
 
0.2%
110 3
 
0.8%
100 25
 
6.2%
90 53
13.2%
80 128
32.0%
70 112
28.0%
60 71
17.8%
50 5
 
1.2%

sg
Categorical

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
1.02
153 
1.01
84 
1.025
81 
1.015
75 
1.005
 
7

Length

Max length5
Median length4
Mean length4.4075
Min length4

Characters and Unicode

Total characters1763
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.02
2nd row1.02
3rd row1.01
4th row1.005
5th row1.01

Common Values

ValueCountFrequency (%)
1.02 153
38.2%
1.01 84
21.0%
1.025 81
20.2%
1.015 75
18.8%
1.005 7
 
1.8%

Length

2023-09-13T23:29:27.460955image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-13T23:29:27.559678image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1.02 153
38.2%
1.01 84
21.0%
1.025 81
20.2%
1.015 75
18.8%
1.005 7
 
1.8%

Most occurring characters

ValueCountFrequency (%)
1 559
31.7%
0 407
23.1%
. 400
22.7%
2 234
13.3%
5 163
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1363
77.3%
Other Punctuation 400
 
22.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 559
41.0%
0 407
29.9%
2 234
17.2%
5 163
 
12.0%
Other Punctuation
ValueCountFrequency (%)
. 400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1763
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 559
31.7%
0 407
23.1%
. 400
22.7%
2 234
13.3%
5 163
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1763
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 559
31.7%
0 407
23.1%
. 400
22.7%
2 234
13.3%
5 163
 
9.2%

al
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9
Minimum0
Maximum5
Zeros245
Zeros (%)61.3%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-09-13T23:29:27.634679image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3131301
Coefficient of variation (CV)1.4590335
Kurtosis0.02278426
Mean0.9
Median Absolute Deviation (MAD)0
Skewness1.1800875
Sum360
Variance1.7243108
MonotonicityNot monotonic
2023-09-13T23:29:27.708784image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 245
61.3%
1 44
 
11.0%
2 43
 
10.8%
3 43
 
10.8%
4 24
 
6.0%
5 1
 
0.2%
ValueCountFrequency (%)
0 245
61.3%
1 44
 
11.0%
2 43
 
10.8%
3 43
 
10.8%
4 24
 
6.0%
5 1
 
0.2%
ValueCountFrequency (%)
5 1
 
0.2%
4 24
 
6.0%
3 43
 
10.8%
2 43
 
10.8%
1 44
 
11.0%
0 245
61.3%

su
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.395
Minimum0
Maximum5
Zeros339
Zeros (%)84.8%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-09-13T23:29:27.781685image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0400381
Coefficient of variation (CV)2.6330078
Kurtosis6.3687605
Mean0.395
Median Absolute Deviation (MAD)0
Skewness2.700055
Sum158
Variance1.0816792
MonotonicityNot monotonic
2023-09-13T23:29:27.853102image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 339
84.8%
2 18
 
4.5%
3 14
 
3.5%
4 13
 
3.2%
1 13
 
3.2%
5 3
 
0.8%
ValueCountFrequency (%)
0 339
84.8%
1 13
 
3.2%
2 18
 
4.5%
3 14
 
3.5%
4 13
 
3.2%
5 3
 
0.8%
ValueCountFrequency (%)
5 3
 
0.8%
4 13
 
3.2%
3 14
 
3.5%
2 18
 
4.5%
1 13
 
3.2%
0 339
84.8%

rbc
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
1
353 
0
47 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters400
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 353
88.2%
0 47
 
11.8%

Length

2023-09-13T23:29:27.932334image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-13T23:29:28.014524image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 353
88.2%
0 47
 
11.8%

Most occurring characters

ValueCountFrequency (%)
1 353
88.2%
0 47
 
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 353
88.2%
0 47
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
Common 400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 353
88.2%
0 47
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 353
88.2%
0 47
 
11.8%

pc
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
1
324 
0
76 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters400
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 324
81.0%
0 76
 
19.0%

Length

2023-09-13T23:29:28.084475image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-13T23:29:28.165466image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 324
81.0%
0 76
 
19.0%

Most occurring characters

ValueCountFrequency (%)
1 324
81.0%
0 76
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 324
81.0%
0 76
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 324
81.0%
0 76
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 324
81.0%
0 76
 
19.0%

pcc
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
0
358 
1
42 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters400
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 358
89.5%
1 42
 
10.5%

Length

2023-09-13T23:29:28.240467image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-13T23:29:28.335473image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 358
89.5%
1 42
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 358
89.5%
1 42
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 358
89.5%
1 42
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
Common 400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 358
89.5%
1 42
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 358
89.5%
1 42
 
10.5%

ba
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
0
378 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters400
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 378
94.5%
1 22
 
5.5%

Length

2023-09-13T23:29:28.421978image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-13T23:29:28.508645image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 378
94.5%
1 22
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 378
94.5%
1 22
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 378
94.5%
1 22
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 378
94.5%
1 22
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 378
94.5%
1 22
 
5.5%

bgr
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct146
Distinct (%)41.0%
Missing44
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean148.03652
Minimum22
Maximum490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-09-13T23:29:28.585702image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile78.75
Q199
median121
Q3163
95-th percentile307.25
Maximum490
Range468
Interquartile range (IQR)64

Descriptive statistics

Standard deviation79.281714
Coefficient of variation (CV)0.53555512
Kurtosis4.2255936
Mean148.03652
Median Absolute Deviation (MAD)25
Skewness2.0107732
Sum52701
Variance6285.5902
MonotonicityNot monotonic
2023-09-13T23:29:28.683652image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 10
 
2.5%
93 9
 
2.2%
100 9
 
2.2%
107 8
 
2.0%
131 6
 
1.5%
140 6
 
1.5%
109 6
 
1.5%
92 6
 
1.5%
117 6
 
1.5%
130 6
 
1.5%
Other values (136) 284
71.0%
(Missing) 44
 
11.0%
ValueCountFrequency (%)
22 1
 
0.2%
70 5
1.2%
74 3
0.8%
75 2
 
0.5%
76 4
1.0%
78 3
0.8%
79 3
0.8%
80 2
 
0.5%
81 3
0.8%
82 3
0.8%
ValueCountFrequency (%)
490 2
0.5%
463 1
0.2%
447 1
0.2%
425 1
0.2%
424 2
0.5%
423 1
0.2%
415 1
0.2%
410 1
0.2%
380 1
0.2%
360 2
0.5%

bu
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct118
Distinct (%)31.0%
Missing19
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean57.425722
Minimum1.5
Maximum391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-09-13T23:29:28.784664image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile17
Q127
median42
Q366
95-th percentile162
Maximum391
Range389.5
Interquartile range (IQR)39

Descriptive statistics

Standard deviation50.503006
Coefficient of variation (CV)0.87944921
Kurtosis9.3452886
Mean57.425722
Median Absolute Deviation (MAD)16
Skewness2.6343745
Sum21879.2
Variance2550.5536
MonotonicityNot monotonic
2023-09-13T23:29:28.885709image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 15
 
3.8%
25 13
 
3.2%
19 11
 
2.8%
40 10
 
2.5%
15 9
 
2.2%
48 9
 
2.2%
50 9
 
2.2%
18 9
 
2.2%
32 8
 
2.0%
49 8
 
2.0%
Other values (108) 280
70.0%
(Missing) 19
 
4.8%
ValueCountFrequency (%)
1.5 1
 
0.2%
10 2
 
0.5%
15 9
2.2%
16 7
1.8%
17 7
1.8%
18 9
2.2%
19 11
2.8%
20 7
1.8%
21 1
 
0.2%
22 6
1.5%
ValueCountFrequency (%)
391 1
0.2%
322 1
0.2%
309 1
0.2%
241 1
0.2%
235 1
0.2%
223 1
0.2%
219 1
0.2%
217 1
0.2%
215 1
0.2%
208 1
0.2%

sc
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct84
Distinct (%)21.9%
Missing17
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean3.0724543
Minimum0.4
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-09-13T23:29:28.992813image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.5
Q10.9
median1.3
Q32.8
95-th percentile11.89
Maximum76
Range75.6
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation5.7411261
Coefficient of variation (CV)1.8685798
Kurtosis79.304345
Mean3.0724543
Median Absolute Deviation (MAD)0.6
Skewness7.5095383
Sum1176.75
Variance32.960529
MonotonicityNot monotonic
2023-09-13T23:29:29.090235image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 40
 
10.0%
1.1 24
 
6.0%
0.5 23
 
5.8%
1 23
 
5.8%
0.9 22
 
5.5%
0.7 22
 
5.5%
0.6 18
 
4.5%
0.8 17
 
4.2%
2.2 10
 
2.5%
1.5 9
 
2.2%
Other values (74) 175
43.8%
(Missing) 17
 
4.2%
ValueCountFrequency (%)
0.4 1
 
0.2%
0.5 23
5.8%
0.6 18
4.5%
0.7 22
5.5%
0.8 17
4.2%
0.9 22
5.5%
1 23
5.8%
1.1 24
6.0%
1.2 40
10.0%
1.3 8
 
2.0%
ValueCountFrequency (%)
76 1
0.2%
48.1 1
0.2%
32 1
0.2%
24 1
0.2%
18.1 1
0.2%
18 1
0.2%
16.9 1
0.2%
16.4 1
0.2%
15.2 1
0.2%
15 1
0.2%

sod
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)10.9%
Missing87
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean137.52875
Minimum4.5
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-09-13T23:29:29.186002image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile125
Q1135
median138
Q3142
95-th percentile150
Maximum163
Range158.5
Interquartile range (IQR)7

Descriptive statistics

Standard deviation10.408752
Coefficient of variation (CV)0.075684188
Kurtosis85.53437
Mean137.52875
Median Absolute Deviation (MAD)3
Skewness-6.9965686
Sum43046.5
Variance108.34212
MonotonicityNot monotonic
2023-09-13T23:29:29.278996image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
135 40
10.0%
140 25
 
6.2%
141 22
 
5.5%
139 21
 
5.2%
142 20
 
5.0%
138 20
 
5.0%
137 19
 
4.8%
150 17
 
4.2%
136 17
 
4.2%
147 13
 
3.2%
Other values (24) 99
24.8%
(Missing) 87
21.8%
ValueCountFrequency (%)
4.5 1
 
0.2%
104 1
 
0.2%
111 1
 
0.2%
113 2
0.5%
114 2
0.5%
115 1
 
0.2%
120 2
0.5%
122 2
0.5%
124 3
0.8%
125 2
0.5%
ValueCountFrequency (%)
163 1
 
0.2%
150 17
4.2%
147 13
3.2%
146 10
 
2.5%
145 11
2.8%
144 9
 
2.2%
143 4
 
1.0%
142 20
5.0%
141 22
5.5%
140 25
6.2%

pot
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)12.8%
Missing88
Missing (%)22.0%
Infinite0
Infinite (%)0.0%
Mean4.6272436
Minimum2.5
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-09-13T23:29:29.372406image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3.4
Q13.8
median4.4
Q34.9
95-th percentile5.7
Maximum47
Range44.5
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation3.1939042
Coefficient of variation (CV)0.69023904
Kurtosis142.50591
Mean4.6272436
Median Absolute Deviation (MAD)0.5
Skewness11.582956
Sum1443.7
Variance10.201024
MonotonicityNot monotonic
2023-09-13T23:29:29.459112image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3.5 30
 
7.5%
5 30
 
7.5%
4.9 27
 
6.8%
4.7 17
 
4.2%
4.8 16
 
4.0%
4 14
 
3.5%
4.1 14
 
3.5%
4.4 14
 
3.5%
3.9 14
 
3.5%
3.8 14
 
3.5%
Other values (30) 122
30.5%
(Missing) 88
22.0%
ValueCountFrequency (%)
2.5 2
 
0.5%
2.7 1
 
0.2%
2.8 1
 
0.2%
2.9 3
 
0.8%
3 2
 
0.5%
3.2 3
 
0.8%
3.3 3
 
0.8%
3.4 5
 
1.2%
3.5 30
7.5%
3.6 8
 
2.0%
ValueCountFrequency (%)
47 1
 
0.2%
39 1
 
0.2%
7.6 1
 
0.2%
6.6 1
 
0.2%
6.5 2
0.5%
6.4 1
 
0.2%
6.3 3
0.8%
5.9 2
0.5%
5.8 2
0.5%
5.7 4
1.0%

hemo
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct115
Distinct (%)33.0%
Missing52
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean12.526437
Minimum3.1
Maximum17.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-09-13T23:29:29.547738image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile7.9
Q110.3
median12.65
Q315
95-th percentile16.9
Maximum17.8
Range14.7
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation2.9125866
Coefficient of variation (CV)0.23251517
Kurtosis-0.47139804
Mean12.526437
Median Absolute Deviation (MAD)2.35
Skewness-0.33509468
Sum4359.2
Variance8.4831608
MonotonicityNot monotonic
2023-09-13T23:29:29.642302image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 16
 
4.0%
10.9 8
 
2.0%
13.6 7
 
1.8%
13 7
 
1.8%
9.8 7
 
1.8%
11.1 7
 
1.8%
10.3 6
 
1.5%
11.3 6
 
1.5%
13.9 6
 
1.5%
12 6
 
1.5%
Other values (105) 272
68.0%
(Missing) 52
 
13.0%
ValueCountFrequency (%)
3.1 1
0.2%
4.8 1
0.2%
5.5 1
0.2%
5.6 1
0.2%
5.8 1
0.2%
6 2
0.5%
6.1 1
0.2%
6.2 1
0.2%
6.3 1
0.2%
6.6 1
0.2%
ValueCountFrequency (%)
17.8 3
0.8%
17.7 1
 
0.2%
17.6 1
 
0.2%
17.5 1
 
0.2%
17.4 2
0.5%
17.3 1
 
0.2%
17.2 2
0.5%
17.1 2
0.5%
17 4
1.0%
16.9 2
0.5%

pcv
Categorical

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)13.3%
Missing70
Missing (%)17.5%
Memory size3.2 KiB
52
 
21
41
 
21
48
 
19
44
 
19
40
 
16
Other values (39)
234 

Length

Max length3
Median length2
Mean length2
Min length1

Characters and Unicode

Total characters660
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)3.0%

Sample

1st row44
2nd row38
3rd row31
4th row32
5th row35

Common Values

ValueCountFrequency (%)
52 21
 
5.2%
41 21
 
5.2%
48 19
 
4.8%
44 19
 
4.8%
40 16
 
4.0%
43 14
 
3.5%
42 13
 
3.2%
45 13
 
3.2%
36 12
 
3.0%
33 12
 
3.0%
Other values (34) 170
42.5%
(Missing) 70
17.5%

Length

2023-09-13T23:29:29.742171image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
52 21
 
6.4%
41 21
 
6.4%
48 19
 
5.8%
44 19
 
5.8%
40 16
 
4.8%
43 15
 
4.5%
42 13
 
3.9%
45 13
 
3.9%
32 12
 
3.6%
50 12
 
3.6%
Other values (33) 169
51.2%

Most occurring characters

ValueCountFrequency (%)
4 175
26.5%
3 129
19.5%
2 96
14.5%
5 71
10.8%
1 41
 
6.2%
0 38
 
5.8%
8 37
 
5.6%
6 28
 
4.2%
9 23
 
3.5%
7 19
 
2.9%
Other values (2) 3
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 657
99.5%
Control 2
 
0.3%
Other Punctuation 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 175
26.6%
3 129
19.6%
2 96
14.6%
5 71
10.8%
1 41
 
6.2%
0 38
 
5.8%
8 37
 
5.6%
6 28
 
4.3%
9 23
 
3.5%
7 19
 
2.9%
Control
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 660
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 175
26.5%
3 129
19.5%
2 96
14.5%
5 71
10.8%
1 41
 
6.2%
0 38
 
5.8%
8 37
 
5.6%
6 28
 
4.2%
9 23
 
3.5%
7 19
 
2.9%
Other values (2) 3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 175
26.5%
3 129
19.5%
2 96
14.5%
5 71
10.8%
1 41
 
6.2%
0 38
 
5.8%
8 37
 
5.6%
6 28
 
4.2%
9 23
 
3.5%
7 19
 
2.9%
Other values (2) 3
 
0.5%

wc
Categorical

HIGH CARDINALITY  MISSING 

Distinct92
Distinct (%)31.2%
Missing105
Missing (%)26.2%
Memory size3.2 KiB
9800
 
11
6700
 
10
9600
 
9
7200
 
9
9200
 
9
Other values (87)
247 

Length

Max length5
Median length4
Mean length4.2271186
Min length2

Characters and Unicode

Total characters1247
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)11.5%

Sample

1st row7800
2nd row6000
3rd row7500
4th row6700
5th row7300

Common Values

ValueCountFrequency (%)
9800 11
 
2.8%
6700 10
 
2.5%
9600 9
 
2.2%
7200 9
 
2.2%
9200 9
 
2.2%
6900 8
 
2.0%
5800 8
 
2.0%
11000 8
 
2.0%
7800 7
 
1.8%
7000 7
 
1.8%
Other values (82) 209
52.2%
(Missing) 105
26.2%

Length

2023-09-13T23:29:29.837565image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9800 11
 
3.7%
6700 10
 
3.4%
9600 9
 
3.1%
7200 9
 
3.1%
9200 9
 
3.1%
6900 8
 
2.7%
5800 8
 
2.7%
11000 8
 
2.7%
7800 7
 
2.4%
7000 7
 
2.4%
Other values (80) 209
70.8%

Most occurring characters

ValueCountFrequency (%)
0 645
51.7%
1 99
 
7.9%
9 75
 
6.0%
6 75
 
6.0%
7 75
 
6.0%
8 67
 
5.4%
5 66
 
5.3%
2 55
 
4.4%
4 50
 
4.0%
3 36
 
2.9%
Other values (2) 4
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1243
99.7%
Control 3
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 645
51.9%
1 99
 
8.0%
9 75
 
6.0%
6 75
 
6.0%
7 75
 
6.0%
8 67
 
5.4%
5 66
 
5.3%
2 55
 
4.4%
4 50
 
4.0%
3 36
 
2.9%
Control
ValueCountFrequency (%)
3
100.0%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1247
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 645
51.7%
1 99
 
7.9%
9 75
 
6.0%
6 75
 
6.0%
7 75
 
6.0%
8 67
 
5.4%
5 66
 
5.3%
2 55
 
4.4%
4 50
 
4.0%
3 36
 
2.9%
Other values (2) 4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 645
51.7%
1 99
 
7.9%
9 75
 
6.0%
6 75
 
6.0%
7 75
 
6.0%
8 67
 
5.4%
5 66
 
5.3%
2 55
 
4.4%
4 50
 
4.0%
3 36
 
2.9%
Other values (2) 4
 
0.3%

rc
Categorical

HIGH CORRELATION  MISSING 

Distinct49
Distinct (%)18.1%
Missing130
Missing (%)32.5%
Memory size3.2 KiB
5.2
 
18
4.5
 
16
4.9
 
14
4.7
 
11
3.9
 
10
Other values (44)
201 

Length

Max length3
Median length3
Mean length2.9518519
Min length1

Characters and Unicode

Total characters797
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)1.9%

Sample

1st row5.2
2nd row3.9
3rd row4.6
4th row4.4
5th row5

Common Values

ValueCountFrequency (%)
5.2 18
 
4.5%
4.5 16
 
4.0%
4.9 14
 
3.5%
4.7 11
 
2.8%
3.9 10
 
2.5%
4.8 10
 
2.5%
4.6 9
 
2.2%
3.4 9
 
2.2%
5.9 8
 
2.0%
5.5 8
 
2.0%
Other values (39) 157
39.2%
(Missing) 130
32.5%

Length

2023-09-13T23:29:29.938411image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5.2 18
 
6.7%
4.5 16
 
5.9%
4.9 14
 
5.2%
4.7 11
 
4.1%
3.9 10
 
3.7%
4.8 10
 
3.7%
4.6 9
 
3.3%
3.4 9
 
3.3%
6.1 8
 
3.0%
3.7 8
 
3.0%
Other values (39) 157
58.1%

Most occurring characters

ValueCountFrequency (%)
. 263
33.0%
5 115
14.4%
4 115
14.4%
3 75
 
9.4%
6 52
 
6.5%
2 48
 
6.0%
9 34
 
4.3%
8 27
 
3.4%
7 26
 
3.3%
1 22
 
2.8%
Other values (3) 20
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 532
66.8%
Other Punctuation 264
33.1%
Control 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 115
21.6%
4 115
21.6%
3 75
14.1%
6 52
9.8%
2 48
9.0%
9 34
 
6.4%
8 27
 
5.1%
7 26
 
4.9%
1 22
 
4.1%
0 18
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 263
99.6%
? 1
 
0.4%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 797
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 263
33.0%
5 115
14.4%
4 115
14.4%
3 75
 
9.4%
6 52
 
6.5%
2 48
 
6.0%
9 34
 
4.3%
8 27
 
3.4%
7 26
 
3.3%
1 22
 
2.8%
Other values (3) 20
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 797
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 263
33.0%
5 115
14.4%
4 115
14.4%
3 75
 
9.4%
6 52
 
6.5%
2 48
 
6.0%
9 34
 
4.3%
8 27
 
3.4%
7 26
 
3.3%
1 22
 
2.8%
Other values (3) 20
 
2.5%

htn
Categorical

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
0
251 
1
147 
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters400
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 251
62.7%
1 147
36.8%
2 2
 
0.5%

Length

2023-09-13T23:29:30.044567image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-13T23:29:30.153500image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 251
62.7%
1 147
36.8%
2 2
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 251
62.7%
1 147
36.8%
2 2
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 251
62.7%
1 147
36.8%
2 2
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 251
62.7%
1 147
36.8%
2 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 251
62.7%
1 147
36.8%
2 2
 
0.5%

dm
Real number (ℝ)

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.31
Minimum0
Maximum5
Zeros3
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-13T23:29:30.252516image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q13
median3
Q34
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.59141731
Coefficient of variation (CV)0.17867592
Kurtosis6.7321875
Mean3.31
Median Absolute Deviation (MAD)0
Skewness-1.0923183
Sum1324
Variance0.34977444
MonotonicityNot monotonic
2023-09-13T23:29:30.376498image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 258
64.5%
4 134
33.5%
0 3
 
0.8%
1 2
 
0.5%
5 2
 
0.5%
2 1
 
0.2%
ValueCountFrequency (%)
0 3
 
0.8%
1 2
 
0.5%
2 1
 
0.2%
3 258
64.5%
4 134
33.5%
5 2
 
0.5%
ValueCountFrequency (%)
5 2
 
0.5%
4 134
33.5%
3 258
64.5%
2 1
 
0.2%
1 2
 
0.5%
0 3
 
0.8%

cad
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
1
362 
2
 
34
0
 
2
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters400
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 362
90.5%
2 34
 
8.5%
0 2
 
0.5%
3 2
 
0.5%

Length

2023-09-13T23:29:30.495495image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-13T23:29:30.628278image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 362
90.5%
2 34
 
8.5%
0 2
 
0.5%
3 2
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 362
90.5%
2 34
 
8.5%
0 2
 
0.5%
3 2
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 362
90.5%
2 34
 
8.5%
0 2
 
0.5%
3 2
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 362
90.5%
2 34
 
8.5%
0 2
 
0.5%
3 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 362
90.5%
2 34
 
8.5%
0 2
 
0.5%
3 2
 
0.5%

appet
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
0
317 
1
82 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters400
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 317
79.2%
1 82
 
20.5%
2 1
 
0.2%

Length

2023-09-13T23:29:30.711380image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-13T23:29:30.813297image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 317
79.2%
1 82
 
20.5%
2 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 317
79.2%
1 82
 
20.5%
2 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 317
79.2%
1 82
 
20.5%
2 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 317
79.2%
1 82
 
20.5%
2 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 317
79.2%
1 82
 
20.5%
2 1
 
0.2%

pe
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
0
323 
1
76 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters400
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 323
80.8%
1 76
 
19.0%
2 1
 
0.2%

Length

2023-09-13T23:29:30.909385image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-13T23:29:31.009384image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 323
80.8%
1 76
 
19.0%
2 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 323
80.8%
1 76
 
19.0%
2 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 323
80.8%
1 76
 
19.0%
2 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 323
80.8%
1 76
 
19.0%
2 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 323
80.8%
1 76
 
19.0%
2 1
 
0.2%

ane
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
0
339 
1
60 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters400
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 339
84.8%
1 60
 
15.0%
2 1
 
0.2%

Length

2023-09-13T23:29:31.100285image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-13T23:29:31.201285image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 339
84.8%
1 60
 
15.0%
2 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 339
84.8%
1 60
 
15.0%
2 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 339
84.8%
1 60
 
15.0%
2 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 339
84.8%
1 60
 
15.0%
2 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 339
84.8%
1 60
 
15.0%
2 1
 
0.2%

classification
Categorical

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
0
248 
2
150 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters400
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 248
62.0%
2 150
37.5%
1 2
 
0.5%

Length

2023-09-13T23:29:31.292283image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-13T23:29:31.395277image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 248
62.0%
2 150
37.5%
1 2
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 248
62.0%
2 150
37.5%
1 2
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 248
62.0%
2 150
37.5%
1 2
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 248
62.0%
2 150
37.5%
1 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 248
62.0%
2 150
37.5%
1 2
 
0.5%

Interactions

2023-09-13T23:29:25.013349image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:13.232497image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:14.341170image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:15.365402image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:16.419939image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:17.454648image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:19.036749image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:20.012749image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:21.007133image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:21.996253image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:22.988576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:23.954040image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:25.102735image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:13.331627image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:14.443725image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:15.456402image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:16.508941image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:17.553205image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:19.122853image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:20.104487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:21.100095image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:22.087719image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:23.075262image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:24.040049image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:25.176370image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:13.420702image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:14.526696image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:15.530832image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:16.595159image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:17.634294image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:19.195368image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:20.182474image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:21.173251image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:22.164376image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:23.155431image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:24.116150image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:25.258982image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:13.503909image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:14.608107image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:15.615297image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:16.679518image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:18.288183image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:19.277677image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:20.266472image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:21.258580image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:22.247669image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:23.239978image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:24.198531image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:25.606684image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:13.584906image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:14.678011image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:15.693592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:16.759118image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:18.361182image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:19.366666image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:20.343646image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:21.337987image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:22.328669image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:23.309300image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:24.278120image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:25.683678image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:13.667408image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:14.755012image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:15.769591image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:16.850628image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:18.436981image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:19.446657image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:20.423180image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:21.420478image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:22.406123image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:23.380395image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:24.358118image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:25.758696image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:13.753389image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:14.835013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:15.850968image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:16.942973image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:18.513268image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:19.528260image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:20.504180image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:21.496836image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:22.485088image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:23.450454image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:24.442180image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:25.840831image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:13.861016image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:14.923845image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:15.942490image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:17.035952image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:18.602728image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:19.625332image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:20.591866image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:21.582953image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:22.572190image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:23.535623image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:24.543482image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:25.922956image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:13.946033image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:15.013812image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:16.048538image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:17.117974image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:18.688092image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:19.714393image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:20.677509image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:21.670952image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:22.659459image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:23.617788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:24.647389image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:26.006799image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:14.034741image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:15.111717image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:16.153876image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:17.202953image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:18.770231image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:19.787721image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:20.766576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:21.760216image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:22.744084image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:23.704175image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:24.749883image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:26.076178image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:14.114591image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:15.190842image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:16.236199image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:17.273169image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:18.849062image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:19.860878image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:20.847204image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:21.831929image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:22.815126image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:23.783703image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:24.834026image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:26.156439image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:14.243170image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:15.286103image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:16.335730image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:17.366592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:18.940427image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:19.938758image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:20.926496image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:21.916945image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:22.901709image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:23.873684image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-13T23:29:24.934350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-09-13T23:29:31.500309image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
idagebpalsubgrbuscsodpothemodmsgrbcpcpccbapcvwcrchtncadappetpeaneclassification
id1.000-0.221-0.248-0.529-0.257-0.351-0.343-0.6010.489-0.0300.682-0.4210.3340.2560.3220.2420.1590.2810.1640.2740.4010.1640.2800.2350.1860.669
age-0.2211.0000.1000.1690.2390.2930.3060.345-0.1360.072-0.2310.3470.0870.0000.0620.1880.0820.0000.1260.0580.2630.0690.1160.1090.1170.245
bp-0.2480.1001.0000.1620.1870.1710.1730.294-0.1320.085-0.2750.1810.1240.1900.2390.1690.1350.4160.0000.4320.2430.0000.1730.0770.1880.292
al-0.5290.1690.1621.0000.3780.3350.3800.486-0.4590.027-0.5510.2900.3000.4290.5820.4690.4200.3330.3070.3790.2820.1320.2090.2770.1590.427
su-0.2570.2390.1870.3781.0000.5540.1680.277-0.1970.041-0.2330.4530.1840.1390.2280.2190.2010.2070.3550.2300.1900.1590.1380.0470.0000.207
bgr-0.3510.2930.1710.3350.5541.0000.1950.359-0.2610.072-0.3490.4770.1860.2120.3310.1790.1070.0000.3200.1910.2970.1250.1430.1020.0000.318
bu-0.3430.3060.1730.3800.1680.1951.0000.703-0.4140.212-0.5920.3530.1510.2670.3300.2100.2310.4540.3080.4090.3160.1370.1620.2040.3070.251
sc-0.6010.3450.2940.4860.2770.3590.7031.000-0.4970.129-0.7260.4650.1120.2130.1880.0000.0000.3790.4060.4770.0930.0000.0470.1720.2500.099
sod0.489-0.136-0.132-0.459-0.197-0.261-0.414-0.4971.0000.0210.511-0.3910.1610.1970.2810.2620.1610.2560.2580.3110.2470.1290.1510.1310.2190.275
pot-0.0300.0720.0850.0270.0410.0720.2120.1290.0211.000-0.0630.1290.0610.0000.1950.0000.0000.5110.2090.5500.0000.0000.0000.0660.0970.000
hemo0.682-0.231-0.275-0.551-0.233-0.349-0.592-0.7260.511-0.0631.000-0.4910.2820.3000.4670.3700.2330.6900.0870.4700.4270.1440.2940.2980.4820.594
dm-0.4210.3470.1810.2900.4530.4770.3530.465-0.3910.129-0.4911.0000.1820.1100.1790.1340.0000.2650.1580.2330.8220.5910.2120.1980.0850.393
sg0.3340.0870.1240.3000.1840.1860.1510.1120.1610.0610.2820.1821.0000.2800.3880.3090.2240.2610.2780.3730.2430.0970.1500.1980.1000.489
rbc0.2560.0000.1900.4290.1390.2120.2670.2130.1970.0000.3000.1100.2801.0000.3650.0750.1600.3600.3750.2920.1230.0780.1450.1870.0830.277
pc0.3220.0620.2390.5820.2280.3310.3300.1880.2810.1950.4670.1790.3880.3651.0000.5080.3130.5340.1930.5210.2840.1630.2670.3440.2520.371
pcc0.2420.1880.1690.4690.2190.1790.2100.0000.2620.0000.3700.1340.3090.0750.5081.0000.2530.3480.2970.3400.1830.1700.1770.0780.1620.267
ba0.1590.0820.1350.4200.2010.1070.2310.0000.1610.0000.2330.0000.2240.1600.3130.2531.0000.2180.4380.2620.0550.1390.1320.1150.0000.175
pcv0.2810.0000.4160.3330.2070.0000.4540.3790.2560.5110.6900.2650.2610.3600.5340.3480.2181.0000.1500.3120.3910.1200.2580.2680.3950.526
wc0.1640.1260.0000.3070.3550.3200.3080.4060.2580.2090.0870.1580.2780.3750.1930.2970.4380.1501.0000.1310.0000.0000.0000.0000.0000.000
rc0.2740.0580.4320.3790.2300.1910.4090.4770.3110.5500.4700.2330.3730.2920.5210.3400.2620.3120.1311.0000.4240.2970.3020.3020.3970.653
htn0.4010.2630.2430.2820.1900.2970.3160.0930.2470.0000.4270.8220.2430.1230.2840.1830.0550.3910.0000.4241.0000.7410.2350.2540.2370.417
cad0.1640.0690.0000.1320.1590.1250.1370.0000.1290.0000.1440.5910.0970.0780.1630.1700.1390.1200.0000.2970.7411.0000.0760.0920.0000.163
appet0.2800.1160.1730.2090.1380.1430.1620.0470.1510.0000.2940.2120.1500.1450.2670.1770.1320.2580.0000.3020.2350.0761.0000.7650.7280.285
pe0.2350.1090.0770.2770.0470.1020.2040.1720.1310.0660.2980.1980.1980.1870.3440.0780.1150.2680.0000.3020.2540.0920.7651.0000.7200.262
ane0.1860.1170.1880.1590.0000.0000.3070.2500.2190.0970.4820.0850.1000.0830.2520.1620.0000.3950.0000.3970.2370.0000.7280.7201.0000.226
classification0.6690.2450.2920.4270.2070.3180.2510.0990.2750.0000.5940.3930.4890.2770.3710.2670.1750.5260.0000.6530.4170.1630.2850.2620.2261.000

Missing values

2023-09-13T23:29:26.306126image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-13T23:29:26.563884image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-13T23:29:26.720795image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idagebpsgalsurbcpcpccbabgrbuscsodpothemopcvwcrchtndmcadappetpeaneclassification
0048.080.01.0201.00.01100121.036.01.2NaNNaN15.44478005.21410000
117.050.01.0204.00.01100NaN18.00.8NaNNaN11.3386000NaN0310000
2262.080.01.0102.03.01100423.053.01.8NaNNaN9.6317500NaN0411010
3348.070.01.0054.00.01010117.056.03.8111.02.511.23267003.91311110
4451.080.01.0102.00.01100106.026.01.4NaNNaN11.63573004.60310000
5560.090.01.0153.00.0110074.025.01.1142.03.212.23978004.41410100
6668.070.01.0100.00.01100100.054.024.0104.04.012.436NaNNaN0310000
7724.080.01.0152.04.01000410.031.01.1NaNNaN12.444690050410100
8852.0100.01.0153.00.01010138.060.01.9NaNNaN10.83396004.01410010
9953.090.01.0202.00.0001070.0107.07.2114.03.79.529121003.71411010
idagebpsgalsurbcpcpccbabgrbuscsodpothemopcvwcrchtndmcadappetpeaneclassification
39039052.080.01.0250.00.0110099.025.00.8135.03.715.05263005.30310002
39139136.080.01.0250.00.0110085.016.01.1142.04.115.64458006.30310002
39239257.080.01.0200.00.01100133.048.01.2147.04.314.84666005.50310002
39339343.060.01.0250.00.01100117.045.00.7141.04.413.05474005.40310002
39439450.080.01.0200.00.01100137.046.00.8139.05.014.14595004.60310002
39539555.080.01.0200.00.01100140.049.00.5150.04.915.74767004.90310002
39639642.070.01.0250.00.0110075.031.01.2141.03.516.55478006.20310002
39739712.080.01.0200.00.01100100.026.00.6137.04.415.84966005.40310002
39839817.060.01.0250.00.01100114.050.01.0135.04.914.25172005.90310002
39939958.080.01.0250.00.01100131.018.01.1141.03.515.85368006.10310002